Synthetic Control by Covariate Balancing Propensity Score for Disaggregated Data

2554 

Contributed Abstract 

Poster 

Yanran Li (1)

(1) N/A, N/A

Yanran Li  
N/A

Yanran Li  
N/A

Traditionally, most quasi-experimental approaches like the synthetic control method (SCM) were developed for relatively small-size panel data (< 1000). In settings with large-scale environmental data with a large number of treated units and untreated units (e.g., from a few to a few hundred treated units with a donor pool size of a few thousand), with a relatively large number of covariate size, it becomes challenging to apply the traditional SCM due to problems of multiplicity of solutions and computational inefficiency. Despite recent developments on the penalized synthetic control method that resolves the issue of multiplicity of solution by adding a nearest neighbor matching (NNM) penalty to the original SC estimator, this methodology is still computationally inefficient for high-dimensional datasets such as ours. On the other hand, casting our SCM problem as a covariate balancing problem using propensity score (CBPS), in implementation we encounter problems of covariate approximation and non-sparsity of solutions. We conducted various simulation studies to compare the CBPS estimator and the penalized SCM estimator, and proposed a new CBPS estimator for disaggregated data.

causal inference|synthetic control method|Covariate Balancing Propensity Score|Disaggregated Data| |

Section on Nonparametric Statistics

Semi or Nonparametric Methods for Data with complex structure